ADVANCING ELECTORAL INTEGRITY AND VOTER PARTICIPATION THROUGH PREDICTIVE ANALYTICS, SOCIAL MEDIA MONITORING, AND BEHAVIORAL SCIENCE APPLICATIONS IN ELECTORAL ADMINISTRATION

Electoral administration increasingly draws on three technological and behavioral currents: predictive analytics for operational planning and risk management, social media monitoring for tracking misinformation and coordinated inauthentic behavior, and behavioral science interventions designed to encourage participation and build resistance to manipulation. These domains have developed largely in parallel, evaluated within separate disciplinary literatures even as electoral management bodies and their technology partners increasingly deploy them together across the electoral cycle. This review consolidates peer-reviewed and grey literature on predictive analytics, social media monitoring, and behavioral science in electoral administration, examining their conceptual foundations, applications, and points of convergence. It finds that evidentiary quality varies markedly across domains, with behavioral science benefiting from a mature experimental tradition while predictive analytics and social media monitoring remain constrained by proprietary methodologies and restricted data access, respectively. Across all three, questions of privacy, algorithmic bias, and uneven regulatory oversight raise concerns about equity and public trust. The review identifies persistent gaps, including limited research beyond established democracies and insufficient frameworks for evaluating integrated applications, and translates these findings into implications for electoral management bodies and policymakers seeking more transparent and accountable deployment.